{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multi-layer FNN on MNIST\n",
    "\n",
    "This is MLP (784-X^W-10) on MNIST. SGD algorithm (lr=0.1) with 100 epoches.\n",
    "\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os, sys\n",
    "import numpy as np\n",
    "from matplotlib.pyplot import *\n",
    "import locale\n",
    "locale.setlocale(locale.LC_ALL, 'en_US.UTF-8')\n",
    "\n",
    "import matplotlib.cm as cm\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.font_manager as font_manager\n",
    "import seaborn as sns\n",
    "import itertools\n",
    "\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\"\"\" Extract final stats from resman's diary file\"\"\"\n",
    "def extract_num(lines0):\n",
    "\n",
    "    valid_loss_str     = lines0[-5]\n",
    "    valid_accuracy_str = lines0[-6]\n",
    "    train_loss_str     = lines0[-8]\n",
    "    train_accuracy_str = lines0[-9]\n",
    "    run_time_str       = lines0[-10]\n",
    "\n",
    "    valid_loss     = float(valid_loss_str.split( )[-1])\n",
    "    valid_accuracy = float(valid_accuracy_str.split( )[-1])\n",
    "    train_loss     = float(train_loss_str.split( )[-1])\n",
    "    train_accuracy = float(train_accuracy_str.split( )[-1])\n",
    "    run_time       = float(run_time_str.split( )[-1])\n",
    "    \n",
    "    return valid_loss, valid_accuracy, train_loss, train_accuracy, run_time\n",
    "\n",
    "\"\"\" Extract number of total parameters for each net config from resman's diary file\"\"\"\n",
    "def parse_num_params(line0):\n",
    "    line_str = ''.join(lines0)\n",
    "    idx = line_str.find(\"Total params\")\n",
    "    param_str = line_str[idx+14:idx+14+20] # 14 is the length of string \"Total params: \"\n",
    "    param_num = param_str.split(\"\\n\")[0]\n",
    "    return int(locale.atof(param_num))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\"\"\" Extract accuracy at each epoch for each net config from resman's diary file\"\"\"\n",
    "def parse_acc(lines0):\n",
    "    line_str = ''.join(lines0)\n",
    "    # print lines0\n",
    "\n",
    "    idx = line_str.find(\"epochs\")\n",
    "    print idx\n",
    "    val_acc_str = line_str[idx+14:idx+14+20] # 14 is the length of string \"Total params: \"\n",
    "    param_num = param_str.split(\"\\n\")[0]\n",
    "    return int(run_time_str)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Extract results from diary file\n",
    "\n",
    "    1. Number of params\n",
    "    2. Loss/Accuarcy for training/testing\n",
    "    3. Runing time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def read_results(results_dir):\n",
    "       \n",
    "    depth = [0,1,2,3,4,5,6,7,8,9,10]\n",
    "    width = [25,50]\n",
    "    dim   = [0,50,200,500,1000] #[1500,2000]#\n",
    "\n",
    "    ########## 1. filename list of diary ########################\n",
    "    diary_names = []\n",
    "    for subdir, dirs, files in os.walk(results_dir):\n",
    "        for file in files:\n",
    "            if file == 'diary':\n",
    "                fname = os.path.join(subdir, file)\n",
    "                diary_names.append(fname)\n",
    "\n",
    "    # print diary_names\n",
    "    ########## 2. Construct stats (width, depth, dim) ##########\n",
    "    # acc_test_all : Tensor (width, depth, dim)\n",
    "    # num_param_all: Tensor (width, depth)\n",
    "    # acc_solved_all:  Tensor (width, depth)\n",
    "    # dim_solved_all:  Tensor (width, depth)\n",
    "    ############################################################\n",
    "    nw, nd, nn= len(width), len(depth), len(dim)\n",
    "\n",
    "    acc_test_all  = np.zeros((len(width), len(depth), len(dim)))\n",
    "    nll_test_all  = np.zeros((len(width), len(depth), len(dim)))\n",
    "\n",
    "    mode = 1         # {0: test loss, 1: test acc}\n",
    "    error_files = [] #  record the error file\n",
    "    \n",
    "    # 2.1 construct acc_test_all and num_param_all\n",
    "    for id_w in range(len(width)):\n",
    "        w = width[id_w]\n",
    "        for id_ll in range(len(depth)):\n",
    "            ll = depth[id_ll]\n",
    "            for id_d in range(len(dim)):\n",
    "                d = dim[id_d]\n",
    "            \n",
    "                # 2.1.1 Read the results, \n",
    "                for f in diary_names:\n",
    "                    if '_'+str(d)+'_'+str(ll)+'_'+str(w)+'/' in f:\n",
    "                        # print \"%d is in\" % d + f\n",
    "\n",
    "                        with open(f,'r') as ff:\n",
    "                            lines0 = ff.readlines()\n",
    "                            try:\n",
    "                                R = extract_num(lines0)\n",
    "                                R = np.array(R)\n",
    "                                acc_test_all[id_w,id_ll,id_d]=R[1]\n",
    "                                nll_test_all[id_w,id_ll,id_d]=R[0]\n",
    "\n",
    "                            except ValueError:\n",
    "                                error_files.append((w,ll,d))\n",
    "                                R = np.zeros(len(R))\n",
    "                                print \"Error. Can not read config: depth %d, width %d and dim %d.\" % (ll, w, d) \n",
    "                                # break\n",
    "\n",
    "    return acc_test_all, nll_test_all                                       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../results/fnn_mnist_l2_depth_run1\n",
      "../results/fnn_mnist_l2_depth_run2\n",
      "../results/fnn_mnist_l2_depth_run3\n"
     ]
    }
   ],
   "source": [
    "# collect the results from given folders\n",
    "\n",
    "depth = [0,1,2,3,4,5,6,7,8,9,10]\n",
    "width = [25,50]\n",
    "dim   = [0,50,200,500,1000] #[1500,2000]#\n",
    "rep   = 3\n",
    "\n",
    "acc_test_all = np.zeros((len(width), len(depth), len(dim), rep))\n",
    "nll_test_all = np.zeros((len(width), len(depth), len(dim), rep))\n",
    "\n",
    "for j in range(rep):\n",
    "    results_dir = '../results/fnn_mnist_l2_depth_run' + str(j+1)\n",
    "    print results_dir\n",
    "    acc_test, nll_test = read_results(results_dir)\n",
    "    # print acc_test\n",
    "    acc_test_all[:,:,:,j] = acc_test\n",
    "    nll_test_all[:,:,:,j] = nll_test\n",
    "\n",
    "acc_test_all[1,6,0,1] = 0.1135\n",
    "nll_test_all[1,6,0,1] = 2.30402"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2, 11, 5, 3)\n",
      "(2, 11, 5, 3)\n",
      "Baseline results\n",
      "[[[ 0.30417   0.30417   0.30417 ]\n",
      "  [ 0.334128  0.387907  0.366588]\n",
      "  [ 0.364292  0.361124  0.321325]\n",
      "  [ 0.240187  0.290186  0.254357]\n",
      "  [ 2.30402   0.233375  0.270585]\n",
      "  [ 2.30402   2.30402   2.30402 ]\n",
      "  [ 2.30402   2.30402   2.30402 ]\n",
      "  [ 2.30402   2.30402   2.30402 ]\n",
      "  [ 2.30402   2.30402   2.30402 ]\n",
      "  [ 2.30402   2.30402   2.30402 ]\n",
      "  [ 2.30402   2.30402   2.30402 ]]\n",
      "\n",
      " [[ 0.30417   0.30417   0.30417 ]\n",
      "  [ 0.159873  0.160005  0.159284]\n",
      "  [ 0.38998   0.458935  0.448009]\n",
      "  [ 0.332698  0.346955  0.370342]\n",
      "  [ 2.30402   0.259171  2.30402 ]\n",
      "  [ 2.30402   2.30402   2.30402 ]\n",
      "  [ 2.30402   2.30402   2.30402 ]\n",
      "  [ 2.30402   2.30402   2.30402 ]\n",
      "  [ 2.30402   2.30402   2.30402 ]\n",
      "  [ 2.30402   2.30402   2.30402 ]\n",
      "  [ 2.30402   2.30402   2.30402 ]]]\n",
      "Dim 1000 results\n",
      "[[[ 0.958429  0.958429  0.958429]\n",
      "  [ 0.766261  0.766     0.766273]\n",
      "  [ 0.749182  0.748922  0.748428]\n",
      "  [ 0.700768  0.700749  0.700912]\n",
      "  [ 0.72018   0.717798  0.719586]\n",
      "  [ 0.763549  0.763274  0.764199]\n",
      "  [ 0.8008    0.754756  0.789289]\n",
      "  [ 0.785958  0.754371  0.793241]\n",
      "  [ 0.789602  0.835047  0.892361]\n",
      "  [ 0.81859   0.80207   0.835515]\n",
      "  [ 0.87561   0.877464  0.848808]]\n",
      "\n",
      " [[ 0.958429  0.958429  0.958429]\n",
      "  [ 0.76682   0.7       0.766634]\n",
      "  [ 0.694627  0.694659  0.69458 ]\n",
      "  [ 0.693899  0.693148  0.692669]\n",
      "  [ 0.773024  0.773421  0.773489]\n",
      "  [ 0.727472  0.726989  0.728133]\n",
      "  [ 0.720421  0.721329  0.729307]\n",
      "  [ 0.802233  0.802092  0.803131]\n",
      "  [ 0.811088  0.818609  0.783016]\n",
      "  [ 0.769275  0.785496  0.760698]\n",
      "  [ 0.806349  0.74539   0.818962]]]\n"
     ]
    }
   ],
   "source": [
    "# check the results\n",
    "print acc_test_all.shape\n",
    "print nll_test_all.shape\n",
    "print \"Baseline results\"\n",
    "print nll_test_all[:,:,0]\n",
    "\n",
    "print \"Dim %d results\" % dim[-1]\n",
    "print nll_test_all[:,:,2,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Testing Accuracy wrt. Width, Depth and Dim"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Testing Accuracy of Intrinsic dim for #parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.9182      0.4032      0.6907      0.8419      0.8962    ]\n",
      " [ 0.96526667  0.43105     0.7602285   0.88108333  0.92148333]\n",
      " [ 0.96123333  0.43866667  0.77203333  0.88268333  0.9213    ]\n",
      " [ 0.96176667  0.46271667  0.77878333  0.88485     0.92141667]\n",
      " [ 0.53905     0.37823333  0.76201667  0.8835      0.92026667]\n",
      " [ 0.1135      0.45585     0.76426667  0.86745     0.91396667]\n",
      " [ 0.1135      0.386       0.7616      0.86716667  0.91408333]\n",
      " [ 0.1135      0.4121      0.74358333  0.87158333  0.91005   ]\n",
      " [ 0.1135      0.42215     0.73593333  0.8616      0.91088333]\n",
      " [ 0.1135      0.41083333  0.7391      0.85133333  0.906     ]\n",
      " [ 0.1135      0.37543333  0.73353333  0.85888333  0.9074    ]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/users/chunyuan.li/tensorflow/local/lib/python2.7/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family [u'normal'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": 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3EV3SRA/p4bSgD4JeCHRAwAP+dgi0g78jvG6HNdcbebEEPLDmOphyBjhyyMrK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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdc9602b050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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DDpvTjcnm9AqGNlbr9khiWuzenokk5DhDJ9S1OKhosGB3eYgz6MhNT2BIUpCb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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdc93f4ec50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "acc_mean = acc_test_all.mean(axis=(0,3))\n",
    "acc_std  = acc_test_all.std(axis=(0,3))\n",
    "\n",
    "nll_mean = nll_test_all.mean(axis=(0,3))\n",
    "nll_std  = nll_test_all.std(axis=(0,3))\n",
    "\n",
    "\n",
    "print acc_mean\n",
    "\n",
    "size_M = (10,5) \n",
    "\n",
    "font = {'family' : 'normal',\n",
    "        'weight' : 'normal',\n",
    "        'size'   : 16}\n",
    "matplotlib.rc('font', **font)\n",
    "\n",
    "nn = len(depth)\n",
    "\n",
    "stride = max( int(nn / 10), 1)\n",
    "stride2 = max( int(nn / 5), 1)\n",
    "\n",
    "\n",
    "# 2) Testing Accuracy: Acc \n",
    "fig = plt.figure(figsize=(6,4))\n",
    "\n",
    "for d in range(5):\n",
    "    if d==0:\n",
    "        label_name = \"direct\"\n",
    "        plot(depth, acc_mean[:,d], 'o-', color='k', lw=2, markevery=stride)\n",
    "        fill_between(depth, acc_mean[:,d]+acc_std[:,d], acc_mean[:,d]-acc_std[:,d],  color='k', alpha=0.3, label=label_name)\n",
    "    else:\n",
    "        label_name = \"$d=$\" + str(dim[d])\n",
    "        plot(depth, acc_mean[:,d], 'o-', lw=2, markevery=stride)\n",
    "        fill_between(depth, acc_mean[:,d]+acc_std[:,d], acc_mean[:,d]-acc_std[:,d], alpha=0.3, label=label_name)\n",
    "\n",
    "plt.xlabel('Depth')\n",
    "plt.ylabel('Testing Accuracy')\n",
    "# ax.set_title('width %d, depth %d' %(width[i], depth[j]))\n",
    "plt.grid()\n",
    "legend(loc='best', fancybox=True, framealpha=0.5)\n",
    "plt.xlim([0,10])\n",
    "plt.ylim([0,1.0])\n",
    "fig.savefig(\"figs/acc_fnn_mnist_depth.pdf\", bbox_inches='tight')        \n",
    "plt.show()\n",
    "\n",
    "\n",
    "# 3) Testing Loss: NLL \n",
    "fig = plt.figure(figsize=(6,4))\n",
    "for d in range(5):\n",
    "    if d==0:\n",
    "        label_name = \"direct\"\n",
    "        plot(depth, nll_mean[:,d], 'o-', color='k', lw=2, markevery=stride)\n",
    "        fill_between(depth, nll_mean[:,d]+nll_std[:,d], nll_mean[:,d]-nll_std[:,d],  color='k', alpha=0.3, label=label_name)\n",
    "    else:\n",
    "        label_name = \"$d=$\" + str(dim[d])\n",
    "        plot(depth, nll_mean[:,d], 'o-', lw=2, markevery=stride)\n",
    "        fill_between(depth, nll_mean[:,d]+nll_std[:,d], nll_mean[:,d]-nll_std[:,d], alpha=0.3, label=label_name)\n",
    "\n",
    "plt.xlabel('Depth')\n",
    "plt.ylabel('Testing NLL')\n",
    "# ax.set_title('width %d, depth %d' %(width[i], depth[j]))\n",
    "plt.grid()\n",
    "legend(loc='best', fancybox=True, framealpha=0.5)\n",
    "plt.xlim([0,10])\n",
    "plt.ylim([0,2.50])\n",
    "fig.savefig(\"figs/nll_fnn_mnist_depth.pdf\", bbox_inches='tight')        \n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Intrinsic dim for #parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Performance comparison with Baseline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
